PolyU IR
 

PolyU Institutional Repository >
Civil and Environmental Engineering >
CEE Theses >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/3455

Title: Short-term traffic forecasting using Hong Kong annual traffic census
Authors: Tang, Yuen-fan
Subjects: Hong Kong Polytechnic University -- Dissertations
Traffic estimation -- China -- Hong Kong -- Mathematical models
Traffic surveys -- China -- Hong Kong
Issue Date: 2002
Publisher: The Hong Kong Polytechnic University
Abstract: In Hong Kong, the Annual Traffic Census (ATC) report is used to present the statistical results of traffic volume on the automatic traffic counter stations in the middle of each year. In the ATC report, the Annual Average Daily Traffic (AADT) is the most useful traffic information for engineers/planners. In practice, for most on-going transport studies, there is always a need to use the most up-to-date traffic data such as AADT for model development and calibration. However, the current-year AADT data are not always available. Obviously, there is a need to continuously update the AADT based on historical ATC data and available current-year partial traffic counts. The short-term traffic flow forecasting is to predict the traffic daily and hourly volumes at a given location in the next time period of near future (say next day or next month etc) using historical and real-time traffic data. This can be incorporated into the Transportation Information System (TIS) or Advanced Traveller Information Systems (ATIS) to provide travellers with accurate and timely information so as to allow them to make the intelligent decisions on travel choices. In August 1999, the Traffic and Transport Survey Division (TTSD) of the Transport Department (TD) commissioned the Review of the Annual Traffic Census project in which works were carried on the investigation of new approaches for data analysis and development of user-friendly computer programs for computation and presentation. In this research, the short-term traffic flow forecasting models are investigated and the user-friendly ATC computer programs are enhanced. This thesis presents four models for short-term prediction of the hourly and daily traffic flows by the day of week and by month as well as the AADT for the whole current year. These four models are developed and based on the Auto-Regressive Integrated Moving-Average (ARIMA), Neural Network (NN), Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML) methods. The historical traffic data and available current-year partial traffic data are the input data used for model development/calibration. The results (both hourly and daily flows) of the four models are compared with the observed data for validation. The daily flows estimated by the four models are used to calculate the AADT for the current year. From the comparison results, the GML and NPR models appear to be more promising and robust for extensive applications for TIS or ATIS. Therefore, the GML and NPR models are incorporated into the enhanced ATC computer programs to provide the off-line short-term traffic forecasting database for the whole Territory of Hong Kong. The model validation is carried out using the up-to-date ATC data. The prediction results of the estimated AADT show that the average errors of all ATC core stations are less than +-10% from the NPR and GML models. The developed NPR and GML models could be used to predict the hourly and daily traffic flows and estimate the AADT for transport model development and calibration. It was found from the validation results that the NPR model is suitable for prediction of traffic flows in Hong Kong ATC stations. The NPR model is likely to react to unexpected changes more effectively than the GML model.
Degree: M.Phil., Dept. of Civil & Structural Engineering, The Hong Kong Polytechnic University, 2002.
Description: 1 v. (various pagings) : ill. ; 30 cm.
PolyU Library Call No.: [THS] LG51 .H577M CSE 2002 Tang
Rights: All rights reserved.
Type: Thesis
URI: http://hdl.handle.net/10397/3455
Appears in Collections:CEE Theses
PolyU Electronic Theses

Files in This Item:

File Description SizeFormat
b1667764x_ir.pdfFor All Users (Non-printable) 9.31 MBAdobe PDFView/Open
b1667764x_link.htmFor PolyU Users 162 BHTMLView/Open



Facebook Facebook del.icio.us del.icio.us LinkedIn LinkedIn


All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
No item in the PolyU IR may be reproduced for commercial or resale purposes.

 

© Pao Yue-kong Library, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Powered by DSpace (Version 1.5.2)  © MIT and HP
Feedback | Privacy Policy Statement | Copyright & Restrictions - Feedback